Best Test Data Management Tools in Africa

Test Data Management solutions like data masking, synthetic data generation, data subsetting, data discovery, database virtualization, data automation are our core business. We see and understand the struggles of software development teams with test data. Personally Identifiable Information? Too large environments? Long waiting times for a test data refresh? We envision to solve these issues: - Obfuscating, generating or masking databases and flat files; - Extracting or filtering specific data content with data subsetting; - Discovering, profiling and analysing solutions for understanding your test data, - Automating, integrating and orchestrating test data provisioning into your CI/CD pipelines and - Cloning, snapshotting and timetraveling throug your test data with database virtualization. We improve and innovate our test data software with the latest technologies every single day to support medium to large size organizations in their Test Data Management.

The BiG EVAL solution platform provides powerful software tools needed to assure and improve data quality during the whole lifecycle of information. BiG EVAL's data quality management and data testing software tools are based on the BiG EVAL platform - a comprehensive code base aimed for high performance and high flexibility data validation. All features provided were built by practical experience based on the cooperation with our customers. Assuring a high data quality during the whole life cycle of your data is a crucial part of your data governance and is very important to get the most business value out of your data. This is where the automation solution BiG EVAL DQM comes in and supports you in all tasks regarding data quality management. Ongoing quality checks validate your enterprise data continuously, provide a quality metric and supports you in solving the quality issues. BiG EVAL DTA lets you automate testing tasks in your data oriented project.

The users of data management software are primarily from enterprise business teams. This requires the data management software to be highly user-friendly, automated and intelligent. Additionally, data management activities must adhere to various industry-specific and data protection related regulatory requirements. Further, data must be adequate, accurate, consistent, of high quality and securely accessible so that business teams can make informed and data-driven strategic business decisons. Enables an integrated approach for data privacy, data quality management, test data management, data analytics and data modeling. Efficiently addresses growing volumes of data efficiently, through service engine-based architecture. Handles niche data processing requirements, beyond out of box functionality, through a user-defined function framework and python adapter. Provides a lean layer of governance surrounding data privacy and data quality management.